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This paper reviews the relationship between international investment agreements (IIAs) and foreign direct investment (FDI). It is organised in two parts. The first part explores the generic structure of IIAs and highlights the components that are particularly relevant to FDI. It then gives an overview of the specific content of investment treaties using a representative treaty sample over the period 1980–2020. The second part is devoted to surveying theoretical and empirical work on the impact of IIAs on FDI, while highlighting issues with the measurement of FDI, missing FDI data and effect identification due to endogeneity problems. A key difference from earlier surveys is that this article differentiates between IIAs depending on their content and takes this differentiation into account in the measurement of IIAs and their effects on FDI. [ABSTRACT FROM AUTHOR]

ABSTRACT: This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane‐Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties. [ABSTRACT FROM AUTHOR]

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This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of and , a dynamic spatial generalized method of moments (GMM) estimator is proposed based on for the spatial autoregressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance than the usual fixed- or random-effects predictors ignoring the nested structure of the data. This is applied to forecasting the productivity of public capital in the private sector using nested panel data of 48 contiguous American states. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper studies the performance of panel unit root tests when spatial effects are present that account for cross-section correlation. Monte Carlo simulations show that there can be considerable size distortions in panel unit root tests when the true specification exhibits spatial error correlation. These tests are applied to a panel data set on net real income from the 1000 largest French communes observed over the period 1985–1998. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]